| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| task_categories: |
| - multiple-choice |
| - visual-question-answering |
| tags: |
| - video |
| - camera-movement |
| - vision-language |
| size_categories: |
| - 1K<n<100K |
| configs: |
| - config_name: real |
| data_files: |
| - split: test |
| path: real_video_test.jsonl |
| - config_name: syn |
| data_files: |
| - split: test |
| path: syn_video_test.jsonl |
| - config_name: binary |
| data_files: |
| - split: test |
| path: binary_test.jsonl |
| --- |
| |
| # ACaM-Bench |
|
|
| **ACaM-Bench** is an atomic, multiple-choice benchmark for evaluating whether vision-language models can recognize fine-grained camera movements in real-world and synthetic videos. It covers a two-level cinematographic taxonomy of 17 atomic camera movement classes spanning translations, rotations, focal-length changes, static shots, and object-centric movements. |
|
|
| ## Splits |
|
|
| | Split | # Items | Task | Source | |
| |---|---:|---|---| |
| | `real` | 1464 | 4-way multiple choice | Curated real-world clips | |
| | `syn` | 1179 | 4-way multiple choice | AI-generated clips (Veo 3.1 fast preview) | |
| | `binary` | 1510 | Yes/No question | Synthetic clips (balanced 755 Yes / 755 No) | |
|
|
|
|
|
|
| > **Note on externally-sourced clips (not redistributed).** Some `real` items are |
| > sourced from other benchmarks and, to respect their original licenses, are **not |
| > redistributed here**. Filenames in the JSONLs match the originals — download the |
| > clips from each source and place them under the paths below: |
| > |
| > | Source | Items | Place under | |
| > |---|---:|---| |
| > | [**CameraBench**](https://huggingface.co/datasets/syCen/CameraBench) | 454 | `real_videos/Camera_Motion_Bench/videos/` | |
| > | [**ShotBench**](https://huggingface.co/datasets/Vchitect/ShotBench) | 359 | `real_videos/ShotBench/video/` | |
| > | [**CineTechBench**](https://huggingface.co/datasets/Xinran0906/CineTechBench) | 79 | `real_videos/CineTechBench/dataset/clips/` | |
| > |
| > The remaining real clips (FavorBench, MotionBench, self-collected) and all |
| > synthetic clips are included in the archives. |
| |
| ## Dataset Structure |
| |
| ``` |
| ACaM-Bench/ |
| ├── real_video_test.jsonl # 1464 entries (4-way MCQ, real) |
| ├── syn_video_test.jsonl # 1179 entries (4-way MCQ, synthetic) |
| ├── binary_test.jsonl # 1510 entries (Yes/No, synthetic) |
| ├── real_videos.zip # real-world video files (unzip in place) |
| ├── syn_videos.zip # synthetic video files, also used by binary (unzip in place) |
| └── train.zip # training videos archive (see "Training data") |
| ``` |
| |
| The video files are distributed as zip archives. Each archive already contains |
| its top-level folder, so **unzip them in the repo root** and the paths in the |
| JSONLs (e.g. `real_videos/foo.mp4`, `syn_videos/bar.mp4`) resolve as-is: |
| |
| ```bash |
| unzip real_videos.zip # -> real_videos/... |
| unzip syn_videos.zip # -> syn_videos/... |
| ``` |
| |
| ## Fields |
| |
| Each line in the JSONL is a JSON object: |
| |
| | Field | Type | Description | |
| |---|---|---| |
| | `image` | string | Relative path to the video file (e.g. `real_videos/foo.mp4`) | |
| | `camera movement` | list[string] | Ground-truth camera movement label(s) | |
| | `question` | string | The natural-language question shown to the model | |
| | `options` | dict | Four answer choices keyed `A`–`D` | |
| | `correct_answer` | string | Letter of the correct option | |
| | `source` | string | Origin of the clip | |
| | `duration` | float | Video duration in seconds (real split only) | |
| |
| ### `binary` split fields |
| |
| The `binary` split uses a simpler schema (no `options` / `correct_answer`): |
| |
| | Field | Type | Description | |
| |---|---|---| |
| | `image` | string | Relative path to the video file (e.g. `syn_videos/foo.mp4`) | |
| | `camera_motion` | list[string] | The motion the question asks about | |
| | `question` | string | A Yes/No question, e.g. "Does the camera perform an arc movement?" | |
| | `label` | string | Ground-truth answer, `Yes` or `No` | |
| | `source` | string | Origin of the clip | |
| |
| ## Training data |
| |
| The training videos are provided as a single archive, `train.zip`. The archive |
| contains the per-source folders directly (e.g. `DeDopShots/`, …), so **extract it |
| into a folder named `training_videos/`** to match the relative paths used by the |
| training annotations: |
| |
| ```bash |
| # download from the dataset repo, then: |
| mkdir -p training_videos |
| unzip train.zip -d training_videos/ |
| # results in training_videos/DeDopShots/..., etc. |
| ``` |
| |
| The accompanying training annotations (`train.json`, released with the code) refer |
| to videos via paths like `training_videos/<source>/<file>.mp4`, which resolve |
| once the archive is extracted as above. |
|
|
| > **Note on externally-sourced training data (not redistributed).** To respect |
| > their original licenses, some training videos are **not redistributed** here — |
| > `train.zip` excludes them. To use the full training set, obtain the clips from |
| > each source and place them under the paths below (filenames in `train.json` |
| > match): |
| > |
| > | Source | Place under | |
| > |---|---| |
| > | [**CameraBench**](https://huggingface.co/datasets/syCen/CameraBench) | `training_videos/CameraBench_train_videos/` | |
| > | [**ShotQA**](https://huggingface.co/datasets/Vchitect/ShotQA) | `training_videos/ShotQA_Training/` | |
| |
| |
